arXiv (All)
open access
Real Images, Worse Judgments: Evaluating Vision-Language Models on Concreteness and Imagery
Visual inputs are often assumed to improve language understanding in multimodal models. We examine this assumption by asking whether vision-language models (VLMs) can distinguish useful visual evidence from incidental image context in lexical judgments. We use human concreteness and imagery ratings
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- It's Not What the Image Shows: Irrelevant Context Destabilises VLM Judges Without Informing Them#1142170
- The Linear Representation Hypothesis for Vision-Language-Action Models#1105555
- The Visual Insensitivity Gap: Diagnosing When Vision-Language Models Fail to Use Visual Evidence#820870
- Slow to See, Slow to Suppress: Understanding the Effects of Modality in Context-Memory Conflicts#819749
- Slow to See, Slow to Suppress: Understanding the Effects of Modality in Context-Memory Conflicts#809753
Record · ID 974439
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive